Bayesian methods represent a large community when it comes to data analysis and statistical modeling. There are numerous applications, ranging from medical sciences to social sciences. The advancement of technology (computer power) has provided a more fruitful application of Bayesian methods. The author presents the freeware WINBUGS as a main software tool for the analysis.

The table of contents gives us a clear idea of what the author intends to cover in the book. The book is really intended for the reader to learn the methods presented and not just merely to remind oneself of them. The book contains theory, examples, computer code and explanations, but it is based on the theoretical concepts.

The theoretical exposition is quite thorough and clear. The presentation style is very inviting. The author chooses to explain methods in an intuitive and narrative way rather than just stating the important concepts. Examples also form a major part of the text. Without them it would just be a dry account of the Bayesian framework. There are indeed numerous examples in every chapter. They are accompanied with the computer code (WINBUGS, mostly) and the appropriate output. Each example is discussed and the output explained. Data and code used in the examples are also available for download (the web site is given in the book). In general I can say that examples provide means for further practical analysis and exploration. A vast number of references is provided at the end of each chapter.

If one is serious about learning the Bayesian statistical methods than one could definitely start with this book, assuming some background knowledge. Appropriate prerequisites would be upper level undergraduate probability, some matrix algebra (not necessary, but helpful) and a familiarity with WINBUGS, R or MATLAB .

Neglecting the computer code presented and along with that the details of the examples greatly diminishes the pedagogical effectiveness of the book, as these serve as the main drivers for learning the theory presented. Exercises, which are provided at the end of each chapter, also greatly help in understanding the concepts. Most of the exercises are applied, i.e. one should use the computer to do them. The author provides some hints and computer code for some exercises.

Overall, I think this is an excellent book for anyone wishing to learn Bayesian modeling. It is adequate for a researchers, graduate students and also upper-level undergraduates.

Ita Cirovic Donev is a PhD candidate at the University of Zagreb. She hold a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical mehods of credit and market risk. Apart from the academic work she does consulting work for financial institutions.

Preface.

Chapter 1 Introduction: The Bayesian Method, its Benefits and Implementation.